Blog 10

THE USE OF OPTIMIZATION MODELS IN PUBLIC-SECTOR PLANNING

By E. Downey Brill, Jr.
Management Science Vol. 25, No. 5, May 1979

Summary

This paper had many similarities to the paper about “wicked” problems that we read at the beginning of the semester. The overall jest of the paper was that optimization models are useful for public-sector planning but should not be viewed as a way of obtaining “the answer.” After outlining the shortcomings and limitations of optimization models, the author presents different uses of optimization models as a means of aiding in the planning process. He proposes that optimization models be used in conjunction with other tools such as simulation models and analytical methods as well as other models. The use of various models and methods helps create numerous different alternative solutions that can be evaluated by the decision makers. The author endorses this outlook on the use of optimization models because he believes that models cannot truly capture the nature of a complex problem and inventive and creative solutions are needed to meet all quantitative and qualitative constraints and priorities. Optimization models cannot truly create inventive solutions since this is creative human process, although they can promote creativity by proposing possible alternatives when the complexity of the problem overwhelms the planner.

Discussion

In the thirty years since this paper was published, computational speed has increased dramatically and optimization methods have evolved; however, this paper is still relevant and probably will be for at least another thirty years if not longer. The reason for its relevancy is that computers cannot be depended on to give “the answer” when solutions deal with non-quantitative variables that are often political and social in nature. That is why high-level human decision makers are needed to choose the best solution from a range of choices. Hopefully it stays this way until I’m not replace by a computer.






GA-QP MODEL TO OPTIMIZE SEWER SYSTEM DESIGN

By Tze-Chin Pan and Jehng-Jung Kao
Journal of Environmental Engineering ASCE, January 2009


Summary

The author uses a combination of genetic algorithm (GA) and quadratic programming (QP) optimization models to find alternative solutions for the design of sewer systems. The decision variables associated with the GA model were coded as chromosomes. Pipe diameters and the location of the pumping stations are decision variables which are expressed as genes within each chromosome. The QP model decision variables are the slopes and buried depths at the downstream ends of the pipes.

Alternatives were also created by using MGA. The author says, “To facilitate the analysis for the selection of an appropriate alternative, various near-optimum design alternatives were generated by applying the MGA method. The purpose of the MGA is to identify maximally different solutions which can still be regarded as good alternatives when compared to the mathematically optimal solution.”

The optimization strategy outlined above was applied to a sewer system for a residential area with a total drainage area of 260 ha, 56 nodes, and 79 links. The models gave numerous design alternatives with various costs for the planner to choose from. In situations where essential factors are excluded, the author finds that the GA-QP and DPPP alternatives may be inappropriate or infeasible and MGA alternatives provide the best solutions.

Discussion

This paper was interesting and its principles may very well be applied to a multitude of systems; however, it seems that the authors ignore many of the things that constrain real-life sewer systems. Most city sewer systems are old and often buried in unfavorable locations. Certain sites or pipe depths proposed by the model may be extremely expensive because they require special equipment to place the pipe or the land use above the pipe makes it difficult to place the pipe. For instance, if the pipe is placed in extremely rocky soil or underneath a major roadway. These addition cost are not factored into the model. In the authors defense, if these expenses were modeled as constraints it would take a lot of addition work and make the model a lot more complex.